CN115005775A

CN115005775A - Method and device for correcting artifacts of near-infrared signal data and storage medium

Info

Publication number: CN115005775A
Application number: CN202210588936.5A
Authority: CN
Inventors: 汪待发; 杨明熹
Original assignee: Danyang Huichuang Medical Equipment Co ltd
Current assignee: Danyang Huichuang Medical Equipment Co ltd
Priority date: 2022-05-26
Filing date: 2022-05-26
Publication date: 2022-09-06

Abstract

The application relates to an artifact correction method and device for near-infrared signal data and a storage medium. The method may include dividing the received near-infrared signal data into small pieces of signal data of a preset length, calculating a fluctuation deviation parameter of the signal data of each small piece, extracting a fluctuation deviation parameter of the signal data of a first previous predetermined proportion in the sequence, determining an artifact recognition threshold based on a representative fluctuation deviation parameter of the extracted fluctuation deviation parameter of the signal data of the first previous predetermined proportion and a threshold magnification, comparing a deviation of the signal data before and after each small piece with the artifact recognition threshold, and recognizing the signal data of the small piece as containing motion artifacts when the deviation exceeds the artifact recognition threshold. The artifact identification threshold determined by the method has high correlation degree with the physiological oscillation part in the signal, and the motion artifact can be accurately identified under the condition that the real physiological signal amplitude is polluted by the motion artifact and even submerged.

Description

Method and device for correcting artifacts of near-infrared signal data and storage medium

Technical Field

The present application relates to the field of medical data processing, and more particularly, to an artifact correction method and apparatus for near-infrared signal data, and a storage medium.

Background

At present, although fNIRS (near infrared brain function imaging technology) is widely used in the research field of neonates. However, as a method with great application prospect in the neonatal population, the interference of motion artifacts, which is a challenge when the method is applied to daily clinical diagnosis, still faces. In the clinical application of fNIRS, the newborn as the subject randomly takes uncontrollable actions during the process of collecting near-infrared signal data due to the fact that the newborn population has no behavior control ability and cannot listen to instructions, and even in a sleeping condition, a large amount of body movement still occurs. However, the frequent relative movement between the probe of the near-infrared brain function imaging device and the scalp of the newborn results in that the optical signals collected by the device may be interfered and damaged, thereby generating dense motion artifacts. Also, motion artifacts in neonatal signal data are more frequent and heavier. Secondly, the skull of the neonate is soft, the fontanel is not closed, other tissues around the brain are relatively thin, and when the signal data of the neonate are collected, the probe can only be lightly attached to the head of the neonate, and external force is not allowed to be applied to the skull. Inadvertent micro-movements of the neonate can cause slippage between the probe and the scalp. Both of these inevitable problems result in motion artifacts contaminating the entire infant's fNIRS signal data.

Due to the factors that the brain and neurovascular coupling of the neonate is still in the development stage, the excitability of the cortex is low, and the like, the physiological oscillation amplitude in the neonatal fNIRS signal data is small. However, the peak or offset amplitude of a large number of motion artifacts in the signal data is much higher than the changes in physiological oscillations of the neonate, resulting in a large difference between the information reflected when the fNIRS signal data is contaminated and the actual activity of the neonatal cerebral cortex. Improper pre-processing procedures or uncorrected motion artifacts often result in false positives or false negatives of the results. Therefore, how to process neonatal signal data containing dense motion artifacts is a challenge for researchers.

Disclosure of Invention

The present application is intended to solve the above technical problems. The present application aims to provide an artifact correction method, apparatus and storage medium for near-infrared signal data, which is particularly suitable for continuously acquired fNIRS signal data, such as neonates, containing dense and irregular motion artifacts, which is capable of accurately identifying motion artifacts in continuous signal data where real physiological signal amplitudes are contaminated or even overwhelmed by motion artifacts, in order to perform artifact correction on a targeted basis.

In one aspect, the present application relates to a method of artifact correction for near infrared signal data. The artifact correction method comprises the following steps: receiving near-infrared signal data; dividing the received near-infrared signal data into small sections of signal data with preset length, and calculating fluctuation deviation parameters of the signal data of each small section; sorting the calculated fluctuation deviation parameters of the signal data of each small section from small to large so as to extract the fluctuation deviation parameters of the signal data of the first front preset proportion in the sorting; determining an artifact recognition threshold based on a representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first-previous predetermined proportion of signal data and a threshold magnification; and comparing the deviation of the signal data before and after each small section with the artifact identification threshold, and identifying the signal data of the small section as containing the motion artifact when the deviation exceeds the artifact identification threshold.

In another aspect, the present application relates to a method of artifact correction of photoelectric signal data reflecting a physiological condition, the photoelectric signal data being acquired continuously. The artifact correction method comprises the following steps: receiving the optical signal data; dividing the received photoelectric signal data into small sections of signal data with preset lengths, and calculating fluctuation deviation parameters of the signal data of each small section; sorting the calculated fluctuation deviation parameters of the signal data of each small section from small to large so as to extract the fluctuation deviation parameters of the signal data of the first front preset proportion in the sorting; determining an artifact recognition threshold based on a representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first previous predetermined proportion of signal data and a threshold magnification; and comparing the deviation of the signal data before and after each small section with the artifact identification threshold, and identifying the signal data of the small section as containing the motion artifact when the deviation exceeds the artifact identification threshold.

In yet another aspect, the present application relates to an artifact correction device for neonatal quiescent near-infrared signal data. The artifact correction device includes: an interface and at least one first processor. The interface is configured to acquire near-infrared signal data of a brain of a neonatal subject in a resting state. The first processor may be configured to receive near infrared signal data. The first processor may be further configured to divide the received near-infrared signal data into small pieces of signal data of a preset length and calculate a fluctuation deviation parameter of the signal data of each small piece. The first processor may be further configured to rank the calculated fluctuation deviation parameters of the signal data of the respective small segments from small to large to extract the fluctuation deviation parameters of the signal data of a first predetermined proportion in the ranking. The first processor may be further configured to determine an artifact identification threshold based on a representative fluctuation deviation parameter of the extracted first previous predetermined proportion of the signal data and a threshold multiplier. The first processor may be further configured to compare a deviation of the signal data before and after each segment to the artifact identification threshold, and identify the signal data of the segment as containing motion artifacts when the deviation exceeds the artifact identification threshold.

In yet another aspect, the present application relates to a non-transitory computer-readable medium having instructions stored thereon. The instructions, when executed by a processor, implement an artifact correction method for near infrared signal data. The method may include dividing the received near-infrared signal data into small pieces of signal data of a preset length and calculating a fluctuation deviation parameter of the signal data of each small piece. The method may further comprise: and sorting the calculated fluctuation deviation parameters of the signal data of the small sections from small to large so as to extract the fluctuation deviation parameters of the signal data of the first preset proportion in the sorting. The method may further include determining an artifact recognition threshold based on a representative fluctuation deviation parameter of the extracted first previous predetermined proportion of the signal data and a threshold magnification. The method may further comprise comparing deviations of the signal data before and after each segment with the artefact-identifying threshold, and identifying the signal data of the segment as containing motion artefacts when the deviations exceed the artefact-identifying threshold.

In yet another aspect, the present application relates to a device for aiding in the analysis of a brain injury condition. The auxiliary analysis device may include: the auxiliary analysis device acquisition module can be configured to acquire near-infrared signal data of the brain of a newborn subject in a resting state; the second processor may be configured to receive near infrared signal data. The second processor may be further configured to divide the received near-infrared signal data into small pieces of signal data of a preset length and calculate a fluctuation deviation parameter of the signal data of each small piece. The second processor may be further configured to rank the calculated fluctuation deviation parameters of the signal data of the respective small segments from small to large to extract the fluctuation deviation parameters of the signal data of the first predetermined proportion in the ranking. The second processor may be further configured to determine an artifact identification threshold based on a representative fluctuation deviation parameter of the extracted first previous predetermined proportion of the signal data and a threshold multiplier. The second processor may be further configured to compare a deviation of the signal data before and after each segment to the artifact identification threshold, and identify the signal data of the segment as containing motion artifacts when the deviation exceeds the artifact identification threshold. The second processor may be further configured to process and analyze the near-infrared signal data with artifact correction performed to assist in the analysis of the brain injury status of the neonatal subject.

Therefore, the near-infrared signal data are firstly divided into small sections of signal data with preset lengths, and then fluctuation deviation parameters of all the small sections of data are calculated and sequenced. A smaller fluctuation deviation parameter represents less or no motion artifacts in the segment of signal data. Therefore, the representative fluctuation deviation parameter is calculated with the small pieces of signal data having the smaller fluctuation deviation parameter, and the artifact recognition threshold is determined based on the representative fluctuation deviation parameter, so that the determined artifact recognition threshold has a high correlation with the physiological oscillation part in the signal data, so that the motion artifact can be accurately recognized in the near-infrared signal data in which the true physiological signal amplitude is contaminated or even submerged by the motion artifact, so as to perform artifact correction on a targeted basis.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

Drawings

In the drawings, which are not necessarily drawn to scale, like reference numerals may depict like parts in different views. Like numbers with letter suffixes or different letter suffixes may represent different instances of similar components. The drawings illustrate various embodiments, generally by way of example and not by way of limitation, and together with the description and claims, serve to explain the disclosed embodiments. Such embodiments are illustrative and not intended to be exhaustive or exclusive embodiments of the present method, apparatus, system, or non-transitory computer-readable medium having stored thereon instructions for carrying out the method.

Fig. 1 illustrates an artifact correction method of near-infrared signal data according to an embodiment of the present application.

Fig. 2 illustrates a process of fitting corrections to signal data for segments identified as containing motion artifacts according to an embodiment of the application.

FIG. 3 illustrates a process for correcting individual spikes in signal data for segments identified as containing motion artifacts according to an embodiment of the present application.

FIG. 4 shows a schematic diagram of an artifact correction process for near infrared signal data according to an embodiment of the application.

FIG. 5 illustrates an artifact waveform diagram of near infrared signal data according to an embodiment of the application.

Fig. 6 illustrates an artifact correction method of photoelectric signal data reflecting physiological conditions according to an embodiment of the application.

FIG. 7 illustrates a schematic block diagram of an apparatus for artifact correction of near infrared signal data in accordance with an embodiment of the present application.

Detailed Description

In order to make the technical solutions of the present application better understood, the present application is described in detail below with reference to the accompanying drawings and the detailed description. The embodiments of the present application will be described in further detail below with reference to the drawings and specific embodiments, but the present application is not limited thereto.

As used in this application, the terms "first," "second," and the like do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element preceding the word covers the element listed after the word, and does not exclude the possibility that other elements are also covered. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

The expressions "first correction signal data", "second correction signal data", and "third correction signal data" and "fourth correction signal data" herein are merely to distinguish signal data for the purpose of expression, and are not intended to limit the number, and "first correction signal data", "second correction signal data", and "third correction signal data" and "fourth correction signal data" may also be the same signal data or different signal data. In each flow described herein, the order of steps shown in the drawings is merely an example, and related steps may be flexibly executed in a different order from that shown in the drawings without affecting the logical relationship of the respective steps.

Fig. 1 illustrates an artifact correction method of near-infrared signal data according to an embodiment of the present application. As shown in fig. 1, an artifact correction method of near infrared signal data may include receiving near infrared signal data at step S11. For example, the near infrared signal data may include, but is not limited to, OD signal (OD) data into which a near infrared optical signal of the acquired subject is converted. The acquired near-infrared optical signal of the subject may be single-channel signal data or multi-channel signal data, which is not limited herein. The examinee can comprise a common examinee and also an examinee with poor behavior control capability and instruction compliance, such as neonates, middle aged and elderly people with cerebral apoplexy and the like, near infrared signal data of the examinee can contain dense and irregular motion artifacts due to a large amount of uncontrollable random actions, and the motion artifacts can pollute or even submerge the waveform of a real physiological signal.

In step S12, the received near-infrared signal data may be divided into small pieces of signal data of a preset length, and a fluctuation deviation parameter of the signal data of each small piece may be calculated. Compared to the robust fluctuation deviation parameter of the near-infrared signal data of longer duration, the fluctuation deviation parameter of the signal data of a small segment or a small segment is more sensitive to the burst artifacts and is less likely to be masked by the operation on the longer entire segment of the signal data, for example, by averaging within a time window. In the case of a period of fNIRS signal data, the motion artifacts may generally occupy only a fraction of the time period. Of the small segments of signal data into which the overall signal data is divided, artifacts exist in some of the small segments, resulting in a significantly larger fluctuation deviation parameter, while artifacts do not exist in the remaining small segments, resulting in a significantly smaller fluctuation deviation parameter. In particular, significantly larger fluctuation deviation parameters characterize more of the artifact-induced disturbing oscillation deviations, while significantly smaller fluctuation deviation parameters characterize more of the true physiological oscillation deviations. In the calculated deviation set, a smaller physiological oscillation deviation indicates that the piece of signal data contains no or less artifacts. The larger physiological oscillation deviation indicates that the signal data contains more artifacts.

In some embodiments, the fluctuating deviation parameter comprises a standard deviation value (STD).

Step S12 may be specifically implemented as follows. Firstly, dividing received near-infrared signal data into small signal data segments with preset length by using a sliding window method, wherein the duration of a sliding window can be set to 4s, and the interval can be set to 2s, namely, the small signal data segments with the signal data length of 4s are taken every 2 s. Then, a standard deviation value (STD) of the signal data of each segment may be calculated. STD may employ various formulas, for example, n pieces of OD signal data in a time period of length n may be averaged, then the square of the difference between each piece of OD signal data and the average is calculated, then the squares of all differences are summed to obtain the sum of squared differences, the sum of squared differences is averaged for length n, and the root of the average is taken as STD. As another example, the standard deviation value (STD) of each small piece of signal data may also be calculated according to formula (1).

Wherein N is ₁ Is the sequence length, x, of the data within the sliding window _(t) Is the t-th OD signal data. Where a fNIRS signal data sampling rate of 10Hz is used, the duration of the 4S window as described above may be such that the signal data meets the requirements for calculating the STD of the small segments of signal data.

In step S13, the calculated fluctuation deviation parameters of the signal data of the respective small segments are sorted from small to large to extract the fluctuation deviation parameters of the signal data of the first predetermined proportion in the sorting.

By ordering the set of calculated fluctuation deviation parameters (such as, but not limited to, STD) of the signal data of the respective patches, it results in the fluctuation deviation parameters of the patch containing artifacts being ranked behind, while the fluctuation deviation parameters of the patch containing few or no artifacts are ranked in front. The signal data of the first preset proportion in the sequence can be extracted, the signal data of small sections with few or no artifacts can be extracted, the representative fluctuation deviation parameter calculated according to the signal data is more approximate to the fluctuation deviation parameter caused by real physiological oscillation, the interference of the amplitude and the frequency of the artifacts can be eliminated, and the signal data is relatively stable. The reference used for artifact identification is more accurate and more interference-resistant. In some embodiments, the first top predetermined proportion in the ordering may be set according to the specifics of the proportion of time periods during which artifacts occur in such near infrared signal data. For example, for near infrared signal data of a resting state of a newborn, the first predetermined proportion in the ranking may be set to be the top 20% -35% (e.g., 30%) in the ranking. For another example, for near-infrared signal data acquired during rehabilitation exercise of middle aged and elderly people with stroke, the first preset proportion in the sequence can be changed to adapt to the specific situation of the proportion of the time period in which the artifact occurs in the application scene. Therefore, the front small section without artifact interference can be screened efficiently, and the situation that the real physiological oscillation cannot be represented correctly and thoroughly due to the defect of the front small section without artifact interference can be avoided.

In step S14, an artifact recognition threshold is determined based on the representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first-previous predetermined proportion of signal data and the threshold magnification. The representative fluctuation deviation parameter may include any one of a mean value, a median value, a root of the mean value of the sum of squares, and the like. For example, the fluctuation deviation parameter of the extracted first previously predetermined proportion of the signal data may be averaged to obtain the representative fluctuation deviation parameter. For a first predetermined proportion of 30%, the fluctuation deviation parameters of the smaller first 30% of the signal data are averaged to obtain representative fluctuation deviation parameters for marking and identifying motion artifacts. And the threshold multiplying power is further included on the basis of representing the fluctuation deviation parameters, so that the detection robustness of the artifact can be improved. The threshold multiplying power can be set according to the specific situation of multiplying power difference between fluctuation deviation parameters caused by artifacts in the near-infrared signal data and fluctuation deviation parameters caused by real physiological oscillation, so that the detection rate of small artifact sections can be ensured, and the false positive rate of the small artifact sections can be reduced. In some embodiments, the threshold magnification may be set to 4-8, such as but not limited to 6.8, for the neonatal resting state near-infrared signal data.

In some embodiments, the obtained representative fluctuation deviation parameter is multiplied by a threshold multiplying factor to obtain an artifact identification threshold. According to the method, the whole segment of near-infrared signal data is divided into small segments of signal data, and fluctuation deviation parameters of all the small segments of signal data are calculated. In the deviation set, the fluctuation deviation parameters of the smaller first 30% of the signal data are averaged to obtain a representative fluctuation deviation parameter, and further an artifact identification threshold value is obtained. Whereas the fluctuation deviation parameter of the smaller first 30% of the signal data represents the deviation (STD) of the (substantially) noise-free part of the physiological oscillation in the signal. The smaller portions are therefore averaged instead of the deviation of the overall signal data and multiplied by the threshold multiplier to calculate the final artifact identification threshold. Therefore, the fluctuation deviation parameter is represented to be more approximate to the fluctuation deviation parameter caused by real physiological oscillation, the interference of the amplitude and the frequency of the artifact can be eliminated, the artifact identification threshold is relatively more stable, the artifact identification threshold is more accurate, and the anti-interference performance is stronger. On the basis, by combining with the threshold multiplying power according with specific conditions, the detection rate of the artifact small sections can be ensured, and the false positive rate of the artifact small sections can be reduced.

In some embodiments, the received near-infrared signal data may be high-pass filtered in a predetermined frequency range before being divided into small signal data segments of a predetermined length, before step S12. Specifically, the preset frequency may be a cut-off frequency of the high-pass filtering, and the cut-off frequency range may be 0.1Hz to 1Hz, or may be other frequency ranges determined according to the received near-infrared signal data, which is not specifically limited herein. The high-pass filtering is carried out to reserve the physiological oscillation information and the motion artifact information to be removed in the near-infrared signal data and remove other irrelevant noises. The filtered signal data is used to estimate the required artifact identification threshold. The method comprises the steps of performing high-pass filtering on received near-infrared signal data before dividing the received near-infrared signal data into small segments of signal data with preset length, determining an artifact identification threshold value based on the signal data after the high-pass filtering, reducing the influence of other irrelevant noises on the artifact identification threshold value, and reserving physiological oscillation information and motion artifact information to be removed in the near-infrared signal data, so that a more accurate artifact identification threshold value can be obtained.

In step S15, deviations in the signal data before and after each segment may be compared to the artifact identification threshold, and when the deviations exceed the artifact identification threshold, the signal data for the segment is identified as containing motion artifacts.

The deviation of the signal data before and after each segment can be calculated in various ways. Note that "before and after" may be within each segment or outside each segment (including the front and rear end points of the time window of each segment). In some embodiments, the STD of the signal data in each segment may be calculated as the deviation of the signal data before and after each segment, for example, the deviation between adjacent signal data may be calculated in turn as the deviation of the signal data before and after each segment, and thus, the STDs of the signal data of each segment that have been calculated in step S12 may be multiplexed, thereby simplifying the calculation. For another example, the deviation between the signal data of the leading and trailing segments of a small segment of data may be calculated as the deviation of the signal data before and after each small segment. In still other embodiments, it may be calculated whether the difference between the signal data for the front position and the signal data for the rear position before and after each small segment exceeds an artifact identification threshold. If so, the motion artifact is marked. For example, the front and rear positions may span exactly the time window corresponding to the small segment, or may be extended forward and backward by a certain margin from the time window. For example, the front and rear positions may be a single position or a plurality of positions, and the signal data may be averaged for the plurality of positions to calculate the difference between the front and rear positions. That is, the signal data at the front position and the signal data at the rear position may be single-point signal data or representative signal data obtained at multiple points.

After detecting the motion-contaminated portion of the near-infrared signal data (i.e., the small segment containing the artifact) based on the artifact recognition threshold, the resulting near-infrared signal data may be represented by the following equation (2):

x(t)＝{x _FM,1 (t),x _MA,1 (t),x _FM,2 (t),x _MA,2 (t),…,x _FM,L (t),x _MA,L (t) } maleFormula (2) wherein x (t) is the time sequence of all channels, x _FM Representing small segments of signal data that are noise-free (i.e., identified as not containing motion artifacts), and x _MA Representing small pieces of signal data containing motion artifacts and L representing a channel.

After the artifacts are identified, the motion artifacts need to be corrected. The artifact correction can be carried out on the original near-infrared signal data of a single brain channel independently, and subsequent integration analysis is carried out after the artifact correction; or performing artifact identification on fused signal data obtained by fusing near-infrared signal data of multiple channels, for example, fusing by averaging and performing artifact correction. Motion artifact components such as spikes (e.g., 51, 53 of fig. 5), slow drift, baseline offsets (e.g., 52 of fig. 5), etc., are corrected for small pieces of signal data, respectively, hereinafter, that are identified as containing motion artifacts. As an example, the present application takes as an example that a fitting method such as a spline interpolation method is used to correct slow drift, baseline shift, etc. in the motion artifact, and then a peak in the motion artifact is corrected. However, this is not limitative, and the order of the correction steps may be changed.

In some embodiments, when the near-infrared signal data is acquired by the near-infrared brain function imaging device, the near-infrared signal data of a plurality of channels can be acquired simultaneously. Preferably, the step S11 receives near-infrared signal data of a single channel to perform motion artifact identification, that is, the artifact correction method according to various embodiments of the present application performs motion artifact identification on the near-infrared signal data of each channel, divides the near-infrared signal data of each channel into small segments of signal data of a preset length, sorts the calculated fluctuation deviation parameters of the signal data of each small segment from small to large, and determines an artifact identification threshold for each channel based on the representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first previous predetermined proportion of signal data and the threshold magnification, so that the accuracy of motion artifact identification is higher.

In other embodiments, the received data at step S11 may also be near-infrared signal data of multiple channels acquired simultaneously by using a near-infrared brain function imaging apparatus, which is not particularly limited in this application.

Fig. 2 illustrates a process of fitting corrections to signal data for segments identified as containing motion artifacts according to an embodiment of the application. As shown in fig. 2, the artifact correction method may include: at step S21, signal data for segments identified as containing motion artifacts may be fitted to obtain fitted signal data. For example, the fitting may employ various fitting algorithms, such as, but not limited to, spline interpolation methods, polynomial curve fitting, etc., so that the fitting simulates slowly varying artifact components such as slow drift, baseline offsets, etc., in motion artifacts. Taking spline interpolation as an example, modeling can be performed by cubic spline interpolation. From the parameter p in the specified range [0,1], a spline function of an appropriate degree is selected to specify the accuracy of interpolation. For example, when p is 0, a least squares straight line fit is applied, and p is 1, natural cubic spline interpolation is performed. In the present application, p-0.99 is used to correct the identified motion artifacts.

In step S22, the corresponding fitted signal data may be subtracted from the signal data of the respective bin identified as containing motion artifacts to obtain first corrected signal data for the respective bin. The signal data fitted by cubic spline interpolation is subtracted from the signal data of the segments identified as containing motion artifacts, resulting in the artifact-free signal data.

At step S23, equalization adjustments may be made to the first correction signal data for each of the shingles based on the immediately preceding segment signal data to obtain second correction signal data for the shingle. Balance adjustment: the baseline data of the first correction signal data may be pulled back to the same or a similar position as the baseline data of the immediately preceding segment signal data of each segment with reference to the baseline data of the immediately preceding segment signal data of each segment to ensure continuity and smoothness of the preceding and succeeding segment signal data. The amplitude levels (e.g., without limitation, averages) before and after the motion artifact segment in the signal data of each small segment may be equalized to reconstruct the entire time series. The reconstructed signal data can be represented by equation (3) as follows:

y(t)＝{x _FM,1 (t),x _SP,1 (t)+a ₁ ,x _FM,2 (t),x _SP,2 (t)+a ₂ ,…,x _FM,L (t),x _SP,L (t)+a _L formula (3)

Wherein y (t) represents signal data after equalization adjustment, x _SP Signal data representing the corresponding segment from which the fitted signal data is subtracted, a represents a parameter for equalization adjustment, and L represents a channel.

Fig. 3 shows a process of determining an artifact identification threshold based on a representative fluctuation deviation parameter of the extracted second previous predetermined proportion of signal data and a threshold magnification, identifying individual spikes in the signal data of the respective small segments containing motion artifacts, and correcting them. As shown in fig. 3, the artifact correction method may include a step S31 of fitting corresponding white gaussian noise signal data based on the extracted second predetermined proportion of signal data for each spike in the signal data of each bin identified as containing motion artifacts. Specifically, the first pre-determined proportion and the second pre-determined proportion may be the same pre-determined proportion or different pre-determined proportions, and are not limited herein. In a preferred embodiment, the first and second pre-determined ratios may be the same pre-determined ratio, such that the corresponding white gaussian noise signal data fitted based on the extracted signal data of the second pre-determined ratio is closer to the signal data of the first pre-determined ratio that does not contain motion artifacts or contains fewer motion artifacts, such that the identified peaks are replaced with the corresponding white gaussian noise signal data without a large deviation from the signal data of the first pre-determined ratio and with a high reliability.

For example, the signal data fitted with gaussian white noise is shown in equation (4) below:

y _GS (t)＝{STD ₁ ×Wgn(t),STD ₂ ×Wgn(t),…,STD _L XWgn (t) } formula (4)

Wherein, y _GS (t) represents white Gaussian noise fitted signal data of all channels, STD represents fluctuation deviation parameter, Wgn (t) represents white Gaussian noiseArray, L then represents a channel, STD ₁ The multiplied by Wgn (t) is fitted white Gaussian noise signal data obtained by multiplying the representative fluctuation deviation parameter of the 1 st channel by the white Gaussian noise sequence.

In step S32, the identified peaks may be replaced with corresponding white gaussian noise signal data to obtain third correction signal data for the small segments.

Spline interpolation and other fitting methods for continuous curves can simulate continuous slowly changing artifact components, but cannot fit abrupt spikes. The operation of eliminating the peak can be further executed to eliminate the signal data of each small segment of the slowly changing artifact components such as slow drift, baseline shift, etc., thereby comprehensively eliminating the artifact components of various distribution frequencies and distribution spans. It should be noted that only the peak in the motion artifact is corrected, and the normal signal data and the signal data after the artifact is removed by spline interpolation are not affected.

In some embodiments, after the peak is replaced with corresponding white gaussian noise signal data, and the signal data for eliminating the peak portion is obtained, a situation that the baseline of the signal data for eliminating the peak portion is shifted relative to the signal data of the preceding and following segments may occur, in which case, the signal data for eliminating the peak portion needs to be equalized again to ensure continuity and smoothness of the signal data of the preceding and following segments.

In some embodiments, the artifact correction method further comprises: in step S33, equalization adjustment is performed on the third correction signal data of each small segment based on the preceding segment signal data immediately adjacent to the small segment to obtain the fourth correction signal data of the small segment.

In other embodiments, the artifact correction method further comprises: after subtracting the corresponding fitting signal data from each small segment of signal data identified as containing motion artifacts to obtain first correction signal data of each small segment, directly fitting corresponding white gaussian noise signal data based on the extracted second pre-determined proportion of signal data, and replacing each peak in the signal data with the white gaussian noise signal data, that is, in the scheme, equalization adjustment is performed only once after replacing the peak with the white gaussian noise signal data.

In step S34, equalization adjustment is performed on the succeeding segment signal data immediately adjacent to each segment based on the fourth correction signal data of each segment to complete artifact correction. The equalization adjustment may refer to the baseline data of the fourth correction signal data, for example, determine the equalization adjustment parameter based on the amplitude average value of the fourth correction signal data, and pull the baseline data of the subsequent segment signal data immediately adjacent to the fourth correction signal data to the same or similar value of the baseline data of the fourth correction signal data, so as to ensure continuity and smoothness of the subsequent segment signal data.

After the replacement, the artifact correction can be completed by adjusting the signal segments before and after the spike noise.

The signal data thus reconstructed can be represented by equation (5):

z _(t) ＝{y _FM,1 (t),y _GS,1 (t)+b ₁ ,y _FM,2 (t),y _GS,2 (t)+b ₂ ,…,y _FM,L (t),y _GS,L (t)+b _L equation (5)

Wherein z is _(t) Signal data, y, representing all channels after correction _FM Signal data representing a non-peaked portion of the signal data, y _GS Signal data representing the portion of the removed spike, b representing a parameter for equalization adjustment, and L representing the channel.

FIG. 4 shows a schematic diagram of an artifact correction process for near infrared signal data according to an embodiment of the application. As shown in fig. 4, an artifact correction method of near-infrared signal data includes: near infrared optical signals of the subject are acquired (41 of fig. 4), and waveform display is performed on a computer (42 of fig. 4). After converting the near-infrared optical signal of the collected examinee into OD signal (OD) data, high-pass filtering is performed using a high-pass filter with a cutoff frequency of 0.75Hz, and then the data is divided into small pieces of signal data (43 in fig. 4) of a preset length, and a fluctuation deviation parameter (44 in fig. 4) of the signal data of each small piece is calculated. The calculated fluctuation deviation parameters of the signal data of the respective small segments are sorted from small to large (45 of fig. 4) to extract the fluctuation deviation parameters of the signal data of the first 30% in the sorting. The artifact recognition threshold is determined based on the representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first 30% of the signal data and the threshold magnification 6.8. The deviation of the signal data before and after each segment is compared to the artifact identification threshold and when the deviation exceeds the artifact identification threshold, the signal data for that segment is identified as containing motion artifacts (46 of fig. 4). And correcting slowly changing track components such as slow drift, baseline drift and the like in the signal data of each small section by adopting a cubic spline interpolation method (such as 52 in figure 5). Then, the peak in the motion artifact is identified (47 in fig. 4), and the identified peak is corrected by a gaussian white noise replacement method (48 in fig. 4), so as to obtain near-infrared signal data (49 in fig. 4) with the motion artifact eliminated.

Although the near-infrared signal data is described as an example, the present invention can also be applied to other continuously acquired photoelectric signal data reflecting physiological conditions, such as, but not limited to, electroencephalogram data of at least one channel, electrocardiograph data of at least one channel, and the like. FIG. 6 illustrates an artifact correction method according to the present application for continuously acquired photo-electric signal data reflecting physiological conditions. As shown in fig. 6, the artifact correction method may include the steps of:

step 61 may be receiving the optoelectronic signal data with a representative fluctuation deviation parameter and a threshold magnification based on the fluctuation deviation parameter of the extracted first-previous predetermined proportion of signal data to extract the fluctuation deviation parameter of the first-previous predetermined proportion of signal data in the ordering.

Step 62 may be dividing the received optoelectronic signal data into small segments of signal data of a preset length, and calculating a fluctuation deviation parameter of the signal data of each small segment.

Step 63 may be to sort the calculated fluctuation deviation parameters of the signal data of the respective small segments from small to large to extract the fluctuation deviation parameters of the signal data of the first predetermined proportion in the sorting.

At step 64, an artifact identification threshold may be determined for a representative fluctuation deviation parameter based on the fluctuation deviation parameter of the extracted first previous predetermined proportion of signal data and a threshold multiplier.

Step 65 may be comparing the deviation of the signal data before and after each segment with the artifact identification threshold, and identifying the signal data of the segment as containing motion artifacts when the deviation exceeds the artifact identification threshold.

The steps of the method for artifact correction of near-infrared signal data according to the embodiments of the present application may also be applied to other continuously collected photoelectric signal data, and will not be described repeatedly herein. For multichannel continuous photoelectric signal data, artifact correction (especially per-channel artifact correction) is respectively carried out on the photoelectric signal data of each channel by adopting the artifact correction method of each embodiment of the application, the artifact of each channel can be efficiently removed, so that a more accurate diagnosis and analysis result can be obtained when disease diagnosis and analysis are carried out on the basis of the corrected data, particularly, the false correlation among the signal data of each channel can be reduced when functional connection parameters are calculated, and the interference on the analysis conclusion is avoided.

Authentication

To verify the effectiveness of the method, the applicant has also verified the method using the simulation data and the acquired real resting near infrared signal data of the newborn, comparing the real HbO (oxyhemoglobin) concentration variation and the corrected HbO concentration variation using the following two indicators: (1) a Pearson correlation coefficient R; (2) root mean square error RMSE, where RMSE is calculated as:

for the simulation data, a (t) _i ) For the change data of oxyhemoglobin concentration after the i-th original artifact-free signal data processing, N ₂ The length of the sequence representing the oxyhemoglobin concentration variation data.

And for the data of the newborn resting state near infrared signal, a (t) _i ) After artificial and manual artifact selection and processingNeonatal oxygen and hemoglobin concentration change data. b (t) _i ) The method is the oxyhemoglobin concentration change data obtained after processing the signal data by different artifact processing methods. The pearson correlation coefficient R is used to evaluate the similarity of different processing methods to established criteria. The closer R is to 1, the better the effect of the artifact handling. The smaller the root mean square error RMSE, the less difference between the result obtained after processing by the artifact processing method used and the standard result.

In order to compare the effectiveness of the method compared with other traditional methods, the applicant also utilizes a wavelet algorithm, spline interpolation and wavelet combination method to perform artifact processing on simulation signal data and collected real neonatal resting state near-infrared signal data, and compares an artifact processing analysis result with an artifact processing analysis result of the method, wherein the result is as follows: aiming at simulation signal data, the artifact processing analysis result of the method is as follows: r is 0.860 ± 0.037; RMSE is 0.196 ± 0.02, and the artifact processing analysis results of the wavelet algorithm: r ═ 0.746 ± 0.069; RMSE is 0.314 +/-0.048, and the spline interpolation combines the artifact processing analysis result of the wavelet: r ═ 0.724 ± 0.096; RMSE ═ 0.282 ± 0.053. Aiming at the collected real neonatal resting state near-infrared signal data, the artifact processing and analyzing result of the method is as follows: r ═ 0.745 ± 0.142; RMSE ═ 0.356 ± 0.339, and the results of the wavelet algorithm artifact processing analysis: r is 0.542 ± 0.222; RMSE is 0.811 ± 0.520, spline interpolation combines the artifact processing analysis results of wavelets: r is 0.555 plus or minus 0.046; RMSE is 0.661 ± 0.373. Therefore, compared with the traditional method, the method has more ideal artifact processing effect.

Since a large amount of artifacts in the signal data of the newborn infant can influence the calculation of the identification threshold, the strategy of the application is to eliminate the influence by dividing the whole signal data into various small segments of signal data and sequencing fluctuation deviation parameters of the signal data of the various small segments so as to obtain a more accurate artifact identification threshold. When the newborn resting state near-infrared signal data containing a large number of artifacts is faced, physiological oscillation of a noise-free part in the signal data is estimated in a self-adaptive mode through the strategy, and the artifacts are objectively identified to the maximum extent based on an artifact identification threshold determined by the strategy. And in this way the threshold multiple is also fixed to a smaller extent, so that signal data containing motion artefacts can be identified more accurately. In addition, in order to solve the problem that the artifacts are still remained after correction, different processing methods are adopted for different types of artifacts. The method firstly removes the baseline drift by using a spline interpolation method, and then replaces the correction peak by using gauss. In this way, the gaussian replacement process is not affected by baseline drift in the signal data, resulting in better artifact correction.

Fig. 7 illustrates an illustrative block diagram of an exemplary artifact correction device for neonatal resting near-infrared signal data according to an embodiment of the application, and as shown in fig. 7, an artifact correction device for neonatal resting near-infrared signal data includes an interface 707 and a first processor 701. Interface 707 may be configured to acquire near infrared signal data of a brain of a neonatal subject in a resting state. The first processor 701 may be configured to perform an artifact correction method of near-infrared signal data according to various embodiments of the present application.

Through this interface 707, the artifact correction means of the neonatal resting state near infrared signal data may be connected to a network (not shown), such as but not limited to a local area network in a hospital or the internet. However, the communication mode implemented by the interface 707 is not limited to a network, and may include NFC, bluetooth, WIFI, and the like; either a wired or wireless connection. Taking the network as an example, the interface 707 may connect the artifact correction device of the resting-state near-infrared signal data of the newborn to external devices such as the resting-state near-infrared signal database 708 and the near-infrared signal data acquisition device 709 of the newborn.

In some embodiments, the artifact correction device may be a dedicated intelligent device or a general purpose intelligent device. For example, the artifact correction device may be a computer customized for near-infrared signal data acquisition and near-infrared signal data processing tasks, or a server placed in the cloud. For example, the artifact correction means may be integrated into the signal acquisition means.

The artifact correction device may include the first processor 701 and the memory 704, and may additionally include at least one of an input/output 702 and an image display 703.

The first processor 701 may be a processing device, such as a microprocessor, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), etc., including one or more general purpose processing devices. More specifically, the first processor 701 may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The first processor 701 may also be one or more special-purpose processing devices, such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a system on a chip (SoC), or the like. As will be appreciated by those skilled in the art, in some embodiments, the first processor 701 may be a dedicated processor rather than a general purpose processor. The first processor 701 may include one or more known processing devices, such as a microprocessor from the Pentium (TM), Core (TM), Xeon (TM) or Itanium (TM) family manufactured by Intel (TM), Turion (TM), Athlon (TM), Sempron (TM), Opteron (TM), FX (TM), Phenom (TM) family manufactured by AMD (TM), or various processors manufactured by Sun Microsystems. The first processor 701 may also include a graphics processing unit, such as from

GPU of (1), manufactured by Nvidia TM

Series, GMA manufactured by Intel, Iris, or Radon, manufactured by AMD. The first processor 701 may also include an accelerated processing unit, such as the Desktop A-4(6, 6) family manufactured by AMD (TM), the Xeon Phi (TM) family manufactured by Intel (TM). The disclosed embodiments are not limited to any type of processor or processor circuit that is otherwise configured to receive near-infrared signal data; dividing the received near-infrared signal data into small sections of signal data with preset length, and calculating fluctuation deviation parameters of the signal data of each small section; to pairSorting the calculated fluctuation deviation parameters of the signal data of each small section from small to large so as to extract the fluctuation deviation parameters of the signal data of the first front preset proportion in the sorting; determining an artifact recognition threshold based on a representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first previous predetermined proportion of signal data and a threshold magnification; the deviation of the signal data before and after each segment is compared to the artifact identification threshold, and when the deviation exceeds the artifact identification threshold, the signal data for the segment is identified as containing motion artifacts, or any other type of signal data is manipulated consistent with the disclosed embodiments. In addition, the term "processor" or "image processor" may include more than one processor, e.g., a multi-core design or multiple processors, each having a multi-core design. The first processor 701 may execute sequences of computer program instructions stored in the memory 704 to perform the various operations, processes, and methods disclosed herein.

The first processor 701 may be communicatively coupled to the memory 704 and configured to execute computer-executable instructions stored therein. The memory 704 may include Read Only Memory (ROM), flash memory, Random Access Memory (RAM), Dynamic Random Access Memory (DRAM) such as synchronous DRAM (sdram) or Rambus DRAM, static memory (e.g., flash memory, static random access memory), etc., on which computer-executable instructions are stored in any format. In some embodiments, the memory 704 may store computer executable instructions of one or more medical treatment programs 705. The computer program instructions may be accessed by the first processor 701, read from a ROM or any other suitable memory location, and loaded into RAM for execution by the first processor 701. For example, the memory 704 may store one or more software applications. The software applications stored in the memory 704 may include, for example, an operating system (not shown) for a general computer system and an operating system for a soft control device.

Further, the memory 704 may store the entire software application or only a portion of the software application (e.g., the medical treatment program 705) that is executable by the first processor 701. In addition, the memory 704 may store signal data after a plurality of correction steps.

Further, the memory 704 may store signal data generated/buffered when the computer program is executed, for example, near-infrared signal data 706 including near-infrared signal data transmitted from the neonatal resting-state near-infrared signal database 708, the near-infrared signal data acquisition device 709, and the like. In some embodiments, the near-infrared signal data 706 may be neonatal resting-state near-infrared signal data, and the medical processing program 705 may implement an artifact correction method for the near-infrared signal data according to various embodiments of the present application, such as dividing the neonatal resting-state near-infrared signal data into small segments of a predetermined length, calculating fluctuation deviation parameters of the signal data of each small segment, sorting the calculated fluctuation deviation parameters, determining an artifact recognition threshold, and the like.

In some embodiments, a near-infrared signal data acquisition device 709 may be provided to exchange near-infrared signal data with the neonatal resting state near-infrared signal database 708, and the memory 704 may communicate with the neonatal resting state near-infrared signal database 708 to obtain near-infrared signal data to be artifact corrected. For example, the near-infrared signal data of the resting state of the newborn collected by the near-infrared signal data collection device 709 may be transmitted to and saved in the resting state near-infrared signal database 708 of the newborn, and the artifact correction device may obtain the near-infrared signal data from the resting state near-infrared signal database 708 of the newborn and perform artifact correction on the near-infrared signal data.

In some embodiments, memory 704 may be in communication with neonatal resting state near-infrared signal database 708 to transmit and store the determined artifact identification threshold along with the artifact correction results for the resulting near-infrared signal data to neonatal resting state near-infrared signal database 708.

In addition to displaying the near-infrared signal data, the image display 703 may display other information, such as second corrected signal data obtained by spline interpolation correction, and fourth corrected signal data obtained by replacing the peak with white gaussian noise. In some embodiments, the image display 703 may be an LCD, CRT, or LED display.

Input/output 702 may be configured to allow artifact correction means to receive and/or transmit signal data. Input/output 702 may include one or more digital and/or analog communication devices that allow the artifact correction device to communicate with a user or other machines and devices. For example, input/output 702 may include a keyboard and mouse that allow a user to provide input.

In some embodiments, the image display 703 may present a user interface so that a user may conveniently and intuitively modify (such as edit, move, modify, etc.) the generated signal data tags using the input/output 702 to communicate with the user interface.

The interface 707 may include a network adapter, cable connector, serial connector, USB connector, parallel connector, high speed data transmission adapter such as fiber optic, USB 6.0, lightning, wireless network adapter such as Wi-Fi adapter, telecom (6G, 4G/LTE, etc.) adapter. The artifact correction means may be connected to the network via an interface 707. The network may provide a Local Area Network (LAN), a wireless network, a cloud computing environment (e.g., as software for a service, as a platform for a service, as an infrastructure for a service, etc.), a client-server, a Wide Area Network (WAN), etc.

In other embodiments, the present application further provides an auxiliary analysis device for brain injury condition, which is used for auxiliary analysis of brain injury condition of a newborn, and the auxiliary analysis device includes: an acquisition module and a second processor, wherein the acquisition module may be configured to acquire near infrared signal data of the brain of a neonatal subject in a resting state. The acquisition module may be implemented in various ways, such as, but not limited to, using a near infrared signal data acquisition device. Part of the functions of the second processor are similar to those of the first processor 701 of the artifact correction device for the neonatal resting-state near-infrared signal data, and the artifact correction method is executed to perform artifact correction on the acquired near-infrared signal data. In addition, the second processor can also process and analyze the near infrared signal data after completing artifact correction so as to perform auxiliary analysis on the brain damage condition of the newborn examinee.

Various operations or functions are described herein that may be implemented as or defined as software code or instructions. Such content may be source code or differential code ("delta" or "patch" code) that may be executed directly ("object" or "executable" form). The software code or instructions may be stored in a computer-readable storage medium and, when executed, may cause a machine to perform the functions or operations described, and includes any mechanism for storing information in a form accessible by a machine (e.g., a computing device, an electronic system, etc.), such as recordable or non-recordable media (e.g., Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.).

The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations to the embodiments will be apparent from consideration of the specification and practice of the disclosed embodiments.

The exemplary methods described herein may be machine or computer-implemented, at least in part. Some examples may include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform a method as described in the above examples. An implementation of such a method may include software code, such as microcode, assembly language code, higher level language code, or the like. Various programs or program modules may be created using various software programming techniques. For example, program segments or program modules may be designed using Java, Python, C + +, assembly language, or any known programming language. One or more of such software portions or modules may be integrated into a computer system and/or computer-readable medium. Such software code may include computer readable instructions for performing various methods. The software code may form part of a computer program product or a computer program module. Further, in one example, the software code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of such tangible computer-readable media may include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, Random Access Memories (RAMs), Read Only Memories (ROMs), and the like.

Moreover, although illustrative embodiments have been described herein, the scope includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations or alterations based on the present application. The elements of the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the life of the application, which examples are to be construed as non-exclusive. Further, the steps of the disclosed methods may be modified in any manner, including by reordering steps or inserting or deleting steps. It is intended, therefore, that the description be regarded as examples only, with a true scope being indicated by the following claims and their full scope of equivalents.

The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments may be utilized by one of ordinary skill in the art in view of the above description. Also, in the above detailed description, various features may be combined together to simplify the present application. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. An artifact correction method for near-infrared signal data, comprising:

receiving near-infrared signal data;

dividing the received near-infrared signal data into small sections of signal data with preset length, and calculating fluctuation deviation parameters of the signal data of each small section;

sorting the calculated fluctuation deviation parameters of the signal data of each small section from small to large so as to extract the fluctuation deviation parameters of the signal data of the first front preset proportion in the sorting;

determining an artifact recognition threshold based on a representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first previous predetermined proportion of signal data and a threshold magnification;

and comparing the deviation of the signal data before and after each small section with the artifact identification threshold, and identifying the signal data of the small section as containing the motion artifact when the deviation exceeds the artifact identification threshold.

2. The artifact correction method according to claim 1, characterized by further comprising:

fitting the signal data of the segments identified as containing motion artifacts to obtain fitted signal data;

subtracting the corresponding fitted signal data from the signal data of each segment identified as containing motion artifacts to obtain first corrected signal data for each segment;

equalization adjustments are made to the first correction signal data for each of the shingles based on the preceding segment signal data immediately adjacent the shingles to obtain second correction signal data for the shingle.

3. The artifact correction method according to claim 1 or 2, characterized by further comprising:

fitting corresponding white gaussian noise signal data based on the extracted second pre-determined proportion of signal data for each spike in the signal data identified as each small segment containing motion artifacts;

and replacing each identified peak with corresponding white gaussian noise signal data to obtain third correction signal data of each small section.

4. The artifact correction method according to claim 3, characterized in that the artifact correction method further comprises:

performing equalization adjustment on the third correction signal data of each small segment based on the previous segment signal data next to the small segment to obtain fourth correction signal data of the small segment;

the artifact correction is done by performing equalization adjustment on the succeeding segment signal data immediately adjacent to each of the small segments based on the fourth correction signal data of each of the small segments.

5. The artifact correction method according to claim 1, wherein the representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first-previous predetermined proportion of signal data includes:

averaging the fluctuation deviation parameters of the extracted first predetermined proportion of signal data to obtain the representative fluctuation deviation parameter.

6. The artifact correction method according to claim 1, wherein the fluctuation deviation parameter includes a standard deviation value (STD), and the first predetermined proportion in the ranking is the first 20% to the first 35% in the ranking.

7. The artifact correction method according to claim 1, further comprising, before dividing into small pieces of signal data of a preset length:

and carrying out high-pass filtering in a preset frequency range on the received near-infrared signal data.

8. An artifact correction method of photoelectric signal data reflecting a physiological condition, the photoelectric signal data being continuously acquired, the artifact correction method comprising:

receiving the optical signal data;

dividing the received photoelectric signal data into small sections of signal data with preset lengths, and calculating fluctuation deviation parameters of the signal data of each small section;

determining an artifact recognition threshold based on a representative fluctuation deviation parameter of the fluctuation deviation parameters of the extracted first-previous predetermined proportion of signal data and a threshold magnification;

9. An artifact correction device of newborn resting state near-infrared signal data, characterized by comprising:

an interface configured to acquire near-infrared signal data of a brain of a neonatal subject in a resting state; and

at least one first processor configured to perform the artifact correction method of any of claims 1-7.

10. A non-transitory computer readable medium having stored thereon instructions which, when executed by a processor, perform the method of artifact correction of near-infrared signal data according to any of claims 1-7.

11. An auxiliary analysis device for brain injury conditions, which is used for auxiliary analysis of brain injury conditions of a newborn, the auxiliary analysis device comprising:

an acquisition module configured to acquire near-infrared signal data of a brain of a neonatal subject in a resting state;

a second processor configured to perform the artifact correction method according to any one of claims 1-7 to perform artifact correction on the near-infrared signal data, an

And processing and analyzing the near infrared signal data after artifact correction to perform auxiliary analysis on the brain damage condition of the newborn examinee.